By Christos Faloutsos (auth.), Takashi Washio, Einoshin Suzuki, Kai Ming Ting, Akihiro Inokuchi (eds.)
This e-book constitutes the refereed lawsuits of the twelfth Pacific-Asia convention on wisdom Discovery and knowledge Mining, PAKDD 2008, held in Osaka, Japan, in may well 2008.
The 37 revised lengthy papers, forty revised complete papers, and 36 revised brief papers offered including 1 keynote speak and four invited lectures have been conscientiously reviewed and chosen from 312 submissions. The papers current new principles, unique learn effects, and useful improvement reviews from all KDD-related components together with info mining, information warehousing, computing device studying, databases, records, wisdom acquisition, computerized medical discovery, info visualization, causal induction, and knowledge-based systems.
Read or Download Advances in Knowledge Discovery and Data Mining: 12th Pacific-Asia Conference, PAKDD 2008 Osaka, Japan, May 20-23, 2008 Proceedings PDF
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Extra info for Advances in Knowledge Discovery and Data Mining: 12th Pacific-Asia Conference, PAKDD 2008 Osaka, Japan, May 20-23, 2008 Proceedings
Sample text
Mining association rules with multiple relations. , Lavraˇc, N. ) ILP 1997. LNCS, vol. 1297, pp. 125–132. Springer, Heidelberg (1997) 16. : Reverse Search for Enumeration. Discrete Applied Mathematics 65(1–3), 21–46 (1996) 17. : Complete mining of frequent patterns from graphs: mining graph data. Machine Learning 50(3), 321–354 (2003) 18. : Frequent Subgraph Discovery. In: Proc. ICDM 2001 (2001) 19. : A Boosting Algorithm for Classification of SemiStructured Text. In: Proc. of EMNLP, pp. 301–308 (2004) 20.
Annotation of an edge contains its own weight in order to specify the evidence of the relation according to the data sources it was derived from. The structure of the knowledge network is rather lightweight, that is it simply consists of vertices and edges, but contains no detailed information of the vertices or edges itself. In order to access this valuable, more detailed information as well, so-called data agents have been implemented. For each annotation, representing a particular kind of information of a certain data source, a data agent is available, which can be used to access the corresponding data source and extract the detailed information for a particular vertex or edge annotation.
C Springer-Verlag Berlin Heidelberg 2008 Cost-Sensitive Classifier Evaluation Using Cost Curves 27 whereas the best a numerical measure can do is to represent the average performance across a set of operating points. Cost curves are perhaps the ideal graphical method in this setting because they directly show performance as a function of the misclassification costs and class distribution. In particular, the x-axis and y-axis of a cost curve plot are defined as follows. The x-axis of a cost curve plot is defined by combining the two misclassification costs and the class distribution—represented by p(+), the probability that a given instance is positive—into a single value, P C(+), using the following formula: P C(+) = p(+)C(−|+) p(+)C(−|+) + (1 − p(+))C(+|−) (1) where C(-|+)is the cost of a false negative and C(+|-)is the cost of a false positive.